Molecular profiling of glioblastoma (GBM) - one of the most lethal of all cancers - has revealed a molecular landscape of altered signal transduction cascades that cluster along a set of druggable core pathways. Yet, drugs designed to target these pathways have failed in the clinic, presumably due to the genetic and functional heterogeneity of the tumor. Single cell analyses may resolve the heterogeneity of GBM in a manner that can point to rationale combination therapies for treating the disease and suppressing resistance. So far, those analyses have been limited to two classes. The first is single-cell genome and transcriptome analysis, which is a discovery-level analysis, but is also noisy and contains limited functional information. The second is single-cell functional proteomic analysis of the altered signaling networks from which drug targets emerge. This tool can provide powerful new ways to look at known biology, but it does not permit discovery. In principle, one wants to know, for each single cell, the molecular code of the cell (the genome), the functionality of that cell (the proteome and metabolome), and the connection between the two ? the transcriptome. This requires single cell discovery science that extends from genomics to biological function. We hypothesize that such a deep and multi-level analysis can unveil how GBM tumors adapt or evolve to develop resistance against targeted therapies, thus guiding the design of successful combination therapies for GBM patients.